Heart disease is the leading cause of death in the modern world. The main contributor to the heart diseases is coronary occlusion, which subsequently leads to ischemic heart disease (IHD) and leads to myocardial compromise that presents itself as a decreased range of displacement of the myocardium and reduced thickening. As such, clinicians often make use of imaging techniques for the assessment of heart motion and the underlying myocardial perfusion. In this regard, the clinicians assess the myocardial motion subjectively by scoring the motion as normal, hypokinetic, akinetic, or dyskinetic. However, the conventional myocardial motility scoring is subjective and suffers from inter- and intra-observer variability [1].
Registration and motion estimation algorithms are significant areas of research in the medical imaging community with the goal of aiding the clinicians to achieve more objective outputs. Nevertheless, in vivo validation of registration techniques is not a trivial task since the ground truth motion field of the cardiac displacement is not known exactly. Older validation techniques such as sonomicrometry or implanted markers are invasive and limited to just one point of the cardiac tissue. Additionally, the surgical implantation of markers may change the local heart motion due to local damage. Comparison with the other modalities such as Tagged MR and TDI can be helpful. However, pixel to pixel inter-modality comparison may need registration because the two images will not fully correspond. One approach to validation is a controlled experimental phantom setup that can simulate the anatomy and physiology of the heart. To date, however, a cardiac phantom has yet to be developed that is capable of mimicking the elasticity, ultrasound, and magnetic properties of both normal and diseased cardiac tissue.